Inverse Analysis Method Research Articles

Development and validation of a local thermal non-equilibrium model for high-temperature thermal energy storage in packed beds

High-temperature thermal energy storage (TES) in packed beds is gaining interest for industrial energy recovery. The wide range of temperature distributions causes significant variations in thermophysical properties of the fluid and solid phases, leading to inaccuracies of classical TES models and heat transfer correlations. The objective of this work is to develop and validate a detailed but pragmatic model accounting for high-temperature effects. Based on a literature survey spanning over several communities interested in high-temperature porous media, we propose a generic local thermal non-equilibrium model for granulate porous media accounting for conservation of mass, momentum and energy (two-equation temperature model). The effective parameters needed to inform the model are the effective thermal conductivities of the different phases and the heat transfer coefficient. An experimental-numerical inverse analysis method is employed to determine these parameters. A dedicated experimental facility has been designed and built to study a model granulate made of glass bead of 16 mm diameter. Experiments are conducted using the Transient Single-Blow Technique (TSBT) by passing hot air (ranging from 293 K to 630 K) through cold particles at various mass flow rates, covering a Reynolds number range of 58 to 252. The new model was implemented in the Porous material Analysis Toolbox based on OpenFoam (PATO) used to compute the transient temperature fields. Two optimization algorithms were employed to determine the parameters by minimizing the error between experimental and simulated temperatures: a Latin Hypercube Sampling (LHS) method, and a local optimization method Adaptive nonlinear least-squares algorithm (NL2SOL). The results indicate that the value of heat transfer coefficient hv in the two-equation model falls in the range of 1.0 × 104∼1.93× 104 W/(m3 K) under the given conditions. The axial dispersion gas thermal conductivity was found to be around 5.9 and 67.1 times higher than the gas thermal conductivity at Peclet numbers of around 55 and 165, respectively. Furthermore, two improved correlations of Nusselt number (Nu=2+1.54Re(T)0.6Pr(T)1/3) and of axial dispersion gas thermal conductivity (kdis,∥=0.00053Re(T)2.21Pr(T)⋅kg) are proposed and validated for a range of Reynolds number from 58 to 252. The overall approach is therefore validated for the model granulate of the study, opening new perspectives towards more precise design and monitoring of high-temperature TES systems.

Research and Application of Early Identification of Geological Hazards Technology in Railway Disaster Prevention and Control: A Case Study of Southeastern Gansu, China

Geological hazards significantly threaten the safety of China’s railway network. As the railway system continues to expand, particularly with the effects of accelerated climate change, approximately 70% of the newly encountered geohazards occur outside of known areas. This study proposes a novel approach that can be applied to railway systems to identify potential geohazards, analyze risk areas, and assess section vulnerability. The methodology uses integrated remote sensing technology to effectively enhance potential railway hazard identification timeliness. It combines kernel density, hotspot, and inverse distance-weighted analysis methods to enhance applicability and accuracy in the risk assessment of railway networks. Using a case study in southeastern Gansu as an example, we identified 3976 potential hazards in the study area, analyzed five areas with high concentrations of hazards, and 11 districts and counties prone to disasters that could threaten the railway network. We accurately located 16 sections and 20 significant landslide hazards on eight railway lines that pose operational risks. The effectiveness of the methodology proposed in this paper has been confirmed through field investigations of significant landslide hazards. This study can provide a scientific basis for the sustainability of the railway network and disaster risk management.

Inverse analysis method of molten shape by deep learning

Most structural damage occurs in welds, and transient stress analysis corresponding to the welding procedure is performed to investigate the cause. To accurately reproduce the stress generated in the welds, it is necessary to accurately reproduce the transient heat input in the welding procedure. Thus, traditionally, inverse analysis on the transient heat input is conducted by focusing on the molten shape of the actual components; i.e., try and error work performing transient temperature distribution analysis assuming heat input until the molten shape is reproduced. Goldak's double-ellipsoidal heat source model was used by most of the researchers. In this case, heat source parameters from experience are assumed, and transient temperature distribution analysis is repeated by changing the parameters until the obtained region above the melting point matches the target molten shape. Recently, machine learning methods have been become effective to improve inverse analysis in structural integrity issues to straight-forward analysis. In this work, a model using the deep learning has been developed that can directly determine the parameters of the heat source model from the welding records and the molten shape, which can omit the inverse analysis of the heat source parameters, which has been a bottleneck in the past. This deep learning model can reduce the time required determining parameters used in FEA for failure analysis of welded components.

A Thorough Investigation of the Dynamic Properties of Granite under Cyclic Loading

We propose a novel inverse analysis method that utilizes shockwaves to detect the operational condition of tested rock. To achieve this back analysis, an in-depth investigation of the dynamic properties of granite specimens was conducted. The dynamic properties of the granite specimens were investigated using a triaxial cyclic loading machine, under different confining pressures, loading frequencies, stress amplitudes, and numbers of cycles, and a dynamic response model was constructed from the test data. The results show that the dynamic elastic modulus increased with the increase in confining pressure, while its damping ratio decreased. The dynamic elastic modulus and damping ratio increased with the increase in loading frequency. As the dynamic stress amplitude increased, the dynamic elastic modulus of the granite increased, but the dynamic damping ratio decreased. As the number of cycles increased, the dynamic elastic modulus and dynamic damping ratio of the granite decreased and gradually stabilized. The modified Duncan–Chang model was used to construct the dynamic response model of the specimens. It is worth saying that the correlation coefficient of the model is low at a loading frequency of 20 Hz. This indicates that the frequency has a greater effect on the dynamic response of the specimen compared with the confining pressure. The conclusions obtained from these tests can be used to study more comprehensively the interaction and causal relationship between different factors, and to prepare for the next steps of tunnel rock stress-state prediction.

FBG array arrangement optimization for impact localization

A method to optimize the arrangement of an fiber Bragg grating (FBG) array is described to address the need to improve the accuracy of impact localization for aerospace vehicles. The sensitive area of the FBG, which is approximately elliptical, is analyzed using finite element simulation and experimental measurement. Based on this analysis, three optimized FBG sensing arrangements are proposed to improve the coverage of the FBG sensitive area at the center, located on the side and corner positions of an experimental test sample when subject to an impact. By employing the inverse problem analysis method in multiple repetitions of impact localization experiments, the accuracy of impact localization is improved from 74% with the traditional 4-corner arrangement to 84%, 88%, and 91% respectively for the three arrangements, demonstrating the effectiveness of the optimized FBG array arrangement method.

Observing Material Properties in Composite Structures from Actual Rotations

The shear deflection effects are traditionally neglected in most structural system identification methods. Unfortunately, this assumption might lead to significant errors in some structures, like deep beams. Although some inverse analysis methods based on the stiffness matrix method, including shear deformation effects, have been presented in the literature, none of these methods are able to deal with actual rotations in their formulations. Recently, the observability techniques, one of the first methods for the inverse analysis of structures, included the shear effects into the system of equations. In this approach, the effects of the shear rotation are neglected. When actual rotations on-site are used to estimate the mechanical properties in the inverse analysis, it can result in serious errors in the observed properties. This characteristic might be especially problematic in structures such as deep beams where only rotations can be measured. To solve this problem and increase the observability techniques’ applicability, this paper proposes a new approach to include the shear rotations into the inverse analysis by observability techniques. This modification is based on the introduction of a new iterative process. To illustrate the applicability and potential of the proposed method, the inverse analysis of several examples of growing complexity is presented.

Characterization of wave propagation in complex composite structures (CCS) using a robust inverse analysis method

This paper presents the application of wave propagation characterization for complex composite structures through the Algebraic K-space Identification technique in the Cartesian coordinate system (AKSI-C). The proposed method is a novel development since the developed methodology uses adequate partial differential algebraic operations to provide a robust and low-cost framework for identifying wave propagation parameters of the structures with multidimensional signals for the first time. Additionally, the proposed method has been experimentally applied to identify the complex wave propagation phenomenon of different complex composite structures: (i) honeycomb sandwich composite structure: variability of orthotropic behavior and damping properties with frequency and direction is identified by wavenumber space, 3D dispersion curves, and damping loss factor surface. Then, the contribution of individual layer properties on the changes in dynamic behavior is studied by estimating transition frequency; (ii) locally resonant meta-structure: the effect of the local resonator-induced band gap on the wave attenuation is investigated; (iii) periodic rib-stiffened composite plates: the inner resonance phenomena and their remarkable elastic wave manipulation ability are explored by designing 3D-printed resonators and identifying the mixed-resonance-induced band gap. The proposed method has been compared with other inverse methods to assess its reliability under complex conditions.

A continuous and long-term in-situ stress measuring method based on fiber optic. Part I: Theory of inverse differential strain analysis

A method for in-situ stress measurement via fiber optics was proposed. The method utilizes the relationship between rock mass elastic parameters and in-situ stress. The approach offers the advantage of long-term stress measurements with high spatial resolution and frequency, significantly enhancing the ability to measure in-situ stress. The sensing casing, spirally wrapped with fiber optic, is cemented into the formation to establish a formation sensing nerve. Injecting fluid into the casing generates strain disturbance, establishing the relationship between rock mass properties and treatment pressure. Moreover, an optimization algorithm is established to invert the elastic parameters of formation via fiber optic strains. In the first part of this paper series, we established the theoretical basis for the inverse differential strain analysis method for in-situ stress measurement, which was subsequently verified using an analytical model. This paper is the fundamental basis for the inverse differential strain analysis method.

Neutrophil extracellular trap increase the risk of sepsis: a two-sample, one-way Mendelian randomization study

To investigate the causal relationship between neutrophil extracellular trap (NET) and sepsis based on Mendelian randomization analysis. The genome wide association study (GWAS) dataset for the NET biomarker myeloperoxidase (MPO)-DNA complex based on Donkel et al. 's Rotterdam study (RS) and GWAS dataset for identifying sepsis from the UK biobank were selected to screen single nucleotide polymorphisms (SNPS) associated with MPO-DNA complex as instrumental variable (IV) for genetic variation, using MPO-DNA complex as exposure factor. Potential causal associations between MPO-DNA complex and the risk of occurrence of sepsis, 28-day death from sepsis, need for intensive care due to sepsis, and 28-day death from sepsis requiring intensive care were analyzed using a two-sample, one-way Mendelian randomization analysis primary analysis method of inverse analysis of variance (IVW). Potential pleiotropy was assessed using the MR Egger regression intercept test. Sensitivity analysis was performed using the "leave one out" test. The GWAS data were obtained from a European population of both sexes, and the screening criteria was based on the three main assumptions of Mendelian randomization, resulting in 22 SNP entering the Mendelian randomization analysis. The results of the Mendelian randomization causal association effect analysis using the IVW method showed that for every standard deviation increase in the level of the MPO-DNA complex, the risk of sepsis increased by approximately 18% [odds ratio (OR) = 1.18, 95% confidence interval (95%CI) was 1.07-1.29, P < 0.001], the risk of 28-day death from sepsis increased by approximately 51% (OR = 1.51, 95%CI was 1.27-1.81, P < 0.001), an increase of approximately 38% in the risk of occurrence of needing intensive care due to sepsis (OR = 1.38, 95%CI was 1.12-1.70, P = 0.002), and an increase of approximately 125% in the risk of 28-day death from sepsis requiring intensive care (OR = 2.25, 95%CI was 1.21-4.18, P = 0.01). MR Egger regression intercept test suggested that there was no horizontal pleiotropy in the included SNP, and the MR-PRESSO test did not find outliers. Sensitivity analysis suggested that the results of Mendelian randomization were robust. Rising NET can increase the risk of sepsis onset, progression and death as derived from Mendelian randomization analysis.

Baryonic Mass Inventory for Galaxies and Rarefied Media from Theory and Observations of Rotation and Luminosity

Available inventories of baryonic mass in the universe are based largely on galactic data and empirical calculations made &gt;20 years ago. Values falling below cosmological estimates underlie proposals that certain rarified gassy regions could have extremely high T, which motivated absorption measurements and hydrodynamic models. Yet, the shortfall remains. We inventory the total baryonic mass, focusing on gravitational interactions and updated measurements. A recent analytical inverse method for analyzing galactic rotation curves quantified how baryon mass and associated volumetric density (ρ) depend on distance (r) from galactic centers. The model is based on the dynamical consequences of the observed oblate shape of galaxies and the Virial Theorem. The parameter-free solution provides ρ(r) ∝ 1/r2 which describes star-rich galactic interiors, gas-rich outer discoids, circumgalactic media, and gradation into intergalactic media. Independent observational determinations of baryonic ρ validate that our 1/r2 result describes baryons alone. This solution shows that total baryonic mass associated with any galaxy is 2.4 to 40 times detectable luminosity, depending on galaxy size and spacing. Luminosity data within 50 Mpc show that Andromeda equivalents separated by ~1 Mpc represent the local universe. Combining the above yields (6 ± 2) × 10−25 kg m−3 for the present-day universe. Three other approaches support this high density: (1) evaluating trends and luminosity data near 1000 Mpc; (2) using a recent estimate for the number of galaxies in the universe; (3) calculating an energy balance. We discuss uncertainties in the critical density. Implications of large baryonic ρ are briefly discussed.

Long-term flexural performance of concrete beams with different pozzolanic materials: experimental and analytical study

ABSTRACT In this paper, a set of concrete beams with different cementitious binders are designed and tested to investigate the time-dependent deflection of concrete beams subjected to flexural load over a period of 1 year. Test parameters mainly included the effect of cementitious binders (silica fume (SF), ground-granulated blast-furnace slag (GGBS), and a combination of SF and GGBS), and polypropylene fibers on the time-dependent deflection of concrete. The specimens used in this study are under drying/unsealed conditions. The drying condition was designed to obtain the combination of the creep and the shrinkage that occurred in the common practice of construction. One of the most important problems in the repair of flexural members of concrete structures is the problem of creep and shrinkage, which is a relatively unknown problem in the literature. Furthermore, the experimental results are selected to validate the proposed models. Prediction of time-dependent deflections under flexural loading is proposed by the inverse analysis (IA) method. Based on the results, in comparison to the control specimen, the long-term deflection increases of 5.9% and decreases of 22.1% and 43.2% for specimens containing GGBS, SF and a combination of SF and GGBS, respectively, at the test age of 383 days.

Biaxial tensile behavior of Ti-6Al-4V under proportional loadings at high strain rates

To study the mechanical performance of materials under dynamic and proportional biaxial tensile loadings, the electromagnetic Hopkinson bar system was employed. Dynamic biaxial tensile experiments were conducted by this novel system. Dynamic uniaxial tensile and compressive tests were also performed. The digital image correlation (DIC) technique combined with the high-speed camera was utilized to analyze the deformation in the gauge section of the improved cruciform specimens. The cruciform specimen design was improved by finite element method (FEM) method to adapt to dynamic biaxial loadings. An inverse analysis (IA) method was adapted to deduce the true stresses in cruciform specimens. Based on the Yoon criterion, a strain rate and stress state dependent yield criterion and a related constitutive model were developed to account for the asymmetric yielding and strengthening behaviors under high strain rates. The new constitutive model was implemented in the FEM analysis and the numerical results agreed well with the experiments.

Rockfill material uncertainty inversion analysis of concrete-faced rockfill dams using stacking ensemble strategy and Jaya optimizer

Numerical simulation of concrete-faced rockfill dams (CFRDs) considering the spatial variability of rockfill has become a popular research topic in recent years. In order to determine uncertain rockfill properties efficiently and reliably, this study developed an uncertainty inversion analysis method for rockfill material parameters using the stacking ensemble strategy and Jaya optimizer. The comprehensive implementation process of the proposed model was described with an illustrative CFRD example. First, the surrogate model method using the stacking ensemble algorithm was used to conduct the Monte Carlo stochastic finite element calculations with reduced computational cost and improved accuracy. Afterwards, the Jaya algorithm was used to inversely calculate the combination of the coefficient of variation of rockfill material parameters. This optimizer obtained higher accuracy and more significant uncertainty reduction than traditional optimizers. Overall, the developed model effectively identified the random parameters of rockfill materials. This study provided scientific references for uncertainty analysis of CFRDs. In addition, the proposed method can be applied to other similar engineering structures.

Optimized inversion method for thermal parameters of concrete dam under the insulated condition

It is crucial to study how thermal characteristics of concrete dam change over time, especially in cold regions, in order to guarantee long-term safety of the engineering projects. In this manuscript, an inverse analysis method by coupling the numerical simulation model and dam safety monitoring data, is proposed to solve the problem that true thermal parameters of the whole structure cannot be obtained by laboratory experiments or field tests of single points. Moreover, a numerical equivalence method of the dam and its insulation layer is introduced, in order to overcome the problem that the thickness of the insulation layer is much smaller than that of the concrete dam, resulting in low accuracy of the simulation model. Thirdly, the particle swarm optimization is introduced, in order to solve the ill-posed problems occurring during the solution process of multi-parameter inverse analysis. Considering the problem that the original particle swarm algorithm is easy to fall into local optimum and stuck edge, this study improves particles velocity and inertia weights. Benchmark function tests are applied to show the performance compared to several typical optimized algorithms. Numerical simulation and engineering verification are used to demonstrate the rationality and feasibility of the proposed method. The cases show that the residual box diagram of two typical measuring points calculated by inversion value is smaller than the median line and mean point calculated by design value. Moreover, the change law of the temperature field of the finite element calculation using the inversion values is closer to the actual measured value than that of the design value. The results indicate that the multi-parameter inverse method is feasible in insulated dam engineering. In addition, the result of the improved PSO optimization converges faster and has a higher fitness value than that of the original algorithm. It suggests that the improved PSO algorithm for thermal parameters inversion has fitting accuracy and rationality for insulated concrete dams in cold regions.

Uncertainty Inverse Analysis of Positioning Accuracy for Error Sources Identification of Industrial Robots

Uncertainties widely exist in modeling parameters of industrial robots due to wear during service. These uncertainties are too small to be directly measured and may severely affect the positioning accuracy of end-effector through multi-stage propagation. An uncertainty inverse analysis method of positioning accuracy is proposed to identify uncertainty of typical structural parameters with the selected optimal position information. The Denavit–Hartenberg method is employed to establish the kinematic model. For the probability uncertainty of modeling parameters, the inverse analysis model, which reflects propagation mapping between uncertain variables and end-effector position response is obtained by first-order second-moment method. The partial-derivative information is used to improve calculation efficiency through removing low-contributing parameters. To further reduce measurement noise interference, orthogonal matching pursuit strategy is presented to search optimal sensor placement with the objectives of maximum coefficient modulus and minimum correlation, then pseudoinverse matrix method is employed to obtain the exact solution of identification equation. Finally, numerical simulation results for 6 degrees-of-freedom robots indicate that the proposed method with improved solution efficiency can accurately identify uncertainties of joint angles within the error 5% under large measurement errors.

Evaluation of optical constants in oxide thin films using machine learning

Abstract This paper describes an inverse analysis method using neural networks on optical spectroscopy, and its application to the quantitative optical constant evaluation. The present method consists of three subprocesses. First, measurable UV–visible spectroscopic quantities were calculated as functions of the optical constants of the solid based on the Tomlin equations [J. Phys. D 1 1667 (1968)] by carefully eliminating the unpractical combinations of optical constants. Second, the backpropagation neural network is trained using the calculated relationships between the measurable quantities and the optical constants. Finally, the trained network is utilized to determine the optical constants from measured responses. The conventional (Newton–Raphson) method tends to require the judgment of a well-experienced analyst, while machine learning shows automatically human-free performance in data conversion.

Ocular Artifact Removal Method Based on the Wavelet and ICA Transform

The electroencephalogram is a promising tool used to unravel the mysteries of the brain. However, such signals are often disturbed by ocular artifacts caused by eye movements. In this study, Independent Component Analysis and Wavelet Transform based ocular artifact removal method, which does not need reference signals, is proposed to obtain signals free from ocular artifacts. With our proposed method, firstly, the ocular artifact regions in the time domain of the signal are detected. Then the signal is decomposed into its components by independent component analysis and independent components containing artifacts are detected. Wavelet transform is only applied to these components with artifact. Zeroing is applied to the parts of the wavelet coefficients obtained as a result of the wavelet transform corresponding to the ocular artifact regions in the time domain. Finally, the clean signal is obtained by inverse Wavelet transform and inverse Independent Component Analysis methods, respectively. The proposed algorithm is tested on a real data set. The results are given in comparison with the method in which the zeroing is applied to the classical independent components. According to the results, it is seen that most of the signal is not affected by the zeroing and the neural part of the EEG signals is successfully preserved.

Numerical investigation on circular and elliptical bulge tests for inverse soft tissue characterization.

The acquisition of insights concerning the mechanobiology of aneurysmatic aortic tissues is an important field of investigation. The complete characterization of aneurysm mechanical behaviour can be carried out by biaxial experimental tests on ex vivo specimens. In literature, several works proposed bulge inflation tests as a valid method to analyse aneurysmatic tissue. Bulge test data processing requires the adoption of digital image correlation and inverse analysis approaches to estimate strain and stress distributions, respectively. In this context, however, the accuracy of inverse analysis method has not been evaluated yet. This aspect appears particularly interesting given the anisotropic behaviour of the soft tissue and the possibility to adopt different die geometries. The goal of this study is to provide an accuracy characterization of the inverse analysis applied to the bulge test technique using a numerical approach. In particular, different cases of bulge inflation were simulated in a finite element environment as a reference. To investigate the effect of tissue anisotropic degree and bulge die geometries (circular and elliptical), different input parameters were considered to obtain multiple test cases. The specimen deformed shapes, resulting from the reference finite element simulations, were then analysed through an inverse analysis approach to produce an estimation of stress distributions. The estimated stresses were, at last, compared with the values from the reference finite element simulations. The results demonstrated that the circular die geometry produces a satisfactory estimation accuracy only under certain conditions of material quasi-isotropy. On the other hand, the choice of an elliptical bulge die was proven to be more suitable for the analysis of anisotropic tissues.

Application of GA-BP in Displacement Force Inverse Analysis and Mechanical Parameter Inversion of Deep Foundation Pits

Aiming at the defects of various existing displacement inverse analysis methods, using the nonlinear mapping ability of neural network and the global random search ability of genetic algorithm, this paper proposes a displacement inverse analysis method based on optimized Genetic Algorithm- Back Propagation (GA-BP) for deep foundation pit support. The method changes the method that BP algorithm relies on the guidance of gradient information to adjust the network weights, but uses the characteristics of global search of genetic algorithm to find the most suitable network connection rights and network structure, etc. to achieve the purpose of optimization. Firstly, the deformation mechanism of deep foundation pit is analyzed, its failure mode is summarized, and the calculation method of lateral rock and soil pressure is sorted out according to the code. The theory and characteristics of BP neural network and genetic algorithm are discussed, and the method of using genetic algorithm to optimize BP neural network is proposed to improve the prediction accuracy. In view of the shortcomings of GA-BP neural network prediction model in training sample pretreatment and hidden layer structure design, the optimal normalization interval was determined by correlation coefficient regression analysis, and the analytical expression of the number of neurons in hidden layer was derived by statistical principle, and the value range of the optimal number of neurons in single hidden layer was proposed. Combined with the actual engineering, the mechanical parameters inversion and displacement force inverse analysis are performed using this method, and the results show that the optimized GA-BP has higher prediction accuracy compared with BP neural network and GA-BP, and the deviation of the displacement prediction value at each depth is kept within 0.2 mm, the absolute error interval width is 0.07 mm, and the maximum relative error is 1.35% at 4.0 m depth.

An analytical solution to predict temperatures of dumbbell-shaped rubber specimens under cyclic deformation

Abstract The objective of this study is to propose an analytical solution that can predict temperatures of dumbbell-shaped rubber specimens under cyclic deformation. To achieve this, first, a new mathematical equation was developed based on a modified Mooney-Rivlin (MR) strain energy function, the pseudo-elasticity theory, and the inverse analysis method. This equation was used to determine the internal heat generation rates of rubber compounds. With heat generation rates, the governing equation of heat conduction and the mathematical expression of boundary conditions were further generated to describe the heat transfer in rubber compounds. Based on these equations, a novel analytical solution was developed—the RTDS solution (a solution to predict Rubber Temperatures in Dumbbell-shaped Specimens). This RTDS solution was used to predict rubber temperatures in dumbbell-shaped specimens under cyclic deformation. The results showed that the RTDS solution took 11.9 seconds to derive the rubber temperature results with an average mean absolute percent error (MAPE) of 9.2% compared with lab recordings. The RTDS solution identified a logarithmic increase in rubber temperatures at rising strain levels, and it also identified an increase in rubber temperatures with the rising strain rates. Moreover, the RTDS solution characterized an inverse proportional relationship between the rubber temperature increments and the ambient temperatures.